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28.
Maity, Biswadip; Donyanavard, Bryan; Surhonne, Anmol; Rahmani, Amir M.; Herkersdorf, Andreas; Dutt, Nikil
SEAMS: Self-optimizing Runtime Manager for Approximate Memory Hierarchies Journal Article
In: ACM Transactions on Embedded Computing Systems (ACM-TECS), 20 (5), 2021.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {SEAMS: Self-optimizing Runtime Manager for Approximate Memory Hierarchies},
author = {Biswadip Maity and Bryan Donyanavard and Anmol Surhonne and Amir M. Rahmani and Andreas Herkersdorf and Nikil Dutt},
url = {https://dl.acm.org/doi/fullHtml/10.1145/3466875},
doi = {10.1145/3466875},
year = {2021},
date = {2021-07-01},
urldate = {2021-07-01},
journal = {ACM Transactions on Embedded Computing Systems (ACM-TECS)},
volume = {20},
number = {5},
abstract = {Memory approximation techniques are commonly limited in scope, targeting individual levels of the memory hierarchy. Existing approximation techniques for a full memory hierarchy determine optimal configurations at design-time provided a goal and application. Such policies are rigid: they cannot adapt to unknown workloads and must be redesigned for different memory configurations and technologies. We propose SEAMS: the first self-optimizing runtime manager for coordinating configurable approximation knobs across all levels of the memory hierarchy. SEAMS continuously updates and optimizes its approximation management policy throughout runtime for diverse workloads. SEAMS optimizes the approximate memory configuration to minimize energy consumption without compromising the quality threshold specified by application developers. SEAMS can (1) learn a policy at runtime to manage variable application quality of service (QoS) constraints, (2) automatically optimize for a target metric within those constraints, and (3) coordinate runtime decisions for interdependent knobs and subsystems. We demonstrate SEAMS’ ability to efficiently provide functions (1)–(3) on a RISC-V Linux platform with approximate memory segments in the on-chip cache and main memory. We demonstrate SEAMS’ ability to save up to 37% energy in the memory subsystem without any design-time overhead. We show SEAMS’ ability to reduce QoS violations by 75% with <5% additional energy.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Memory approximation techniques are commonly limited in scope, targeting individual levels of the memory hierarchy. Existing approximation techniques for a full memory hierarchy determine optimal configurations at design-time provided a goal and application. Such policies are rigid: they cannot adapt to unknown workloads and must be redesigned for different memory configurations and technologies. We propose SEAMS: the first self-optimizing runtime manager for coordinating configurable approximation knobs across all levels of the memory hierarchy. SEAMS continuously updates and optimizes its approximation management policy throughout runtime for diverse workloads. SEAMS optimizes the approximate memory configuration to minimize energy consumption without compromising the quality threshold specified by application developers. SEAMS can (1) learn a policy at runtime to manage variable application quality of service (QoS) constraints, (2) automatically optimize for a target metric within those constraints, and (3) coordinate runtime decisions for interdependent knobs and subsystems. We demonstrate SEAMS’ ability to efficiently provide functions (1)–(3) on a RISC-V Linux platform with approximate memory segments in the on-chip cache and main memory. We demonstrate SEAMS’ ability to save up to 37% energy in the memory subsystem without any design-time overhead. We show SEAMS’ ability to reduce QoS violations by 75% with <5% additional energy.
27.
Montgomery, Robert M; Brandysky, Lamar; Neary, Martha; Eikey, Elizabeth; Mark, Gloria; Schneider, Margaret; Stadnick, Nicole A; Zheng, Kai; Mukamel, Dana B; Sorkin, Dara H; Schueller, Stephen M
Curating the Digital Mental Health Landscape With a Guide to Behavioral Health Apps: A County-Driven Resource Journal Article
In: Psychiatric Services, 72 (10), pp. 1229-1232, 2021.
Abstract | Links | BibTeX | Tags:
@article{montgomery2021curating,
title = {Curating the Digital Mental Health Landscape With a Guide to Behavioral Health Apps: A County-Driven Resource},
author = {Robert M Montgomery and Lamar Brandysky and Martha Neary and Elizabeth Eikey and Gloria Mark and Margaret Schneider and Nicole A Stadnick and Kai Zheng and Dana B Mukamel and Dara H Sorkin and Stephen M Schueller},
url = {https://pubmed.ncbi.nlm.nih.gov/34030454/},
doi = {10.1176/appi.ps.202000803},
year = {2021},
date = {2021-05-01},
urldate = {2021-01-01},
journal = {Psychiatric Services},
volume = {72},
number = {10},
pages = {1229-1232},
publisher = {Am Psychiatric Assoc},
abstract = {With more than 10,000 mental health apps available, consumers and clinicians who want to adopt such tools can be overwhelmed by the multitude of options and lack of clear evaluative standards. Despite the increasing prevalence of curated lists, or app guides, challenges remain. Organizations providing mental health services to consumers have an opportunity to address these challenges by producing guides that meet relevant standards of quality and are tailored to local needs. This column summarizes an example of the collaborative process of app guide development in a publicly funded mental health service context and highlights opportunities and barriers identified through the process.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
With more than 10,000 mental health apps available, consumers and clinicians who want to adopt such tools can be overwhelmed by the multitude of options and lack of clear evaluative standards. Despite the increasing prevalence of curated lists, or app guides, challenges remain. Organizations providing mental health services to consumers have an opportunity to address these challenges by producing guides that meet relevant standards of quality and are tailored to local needs. This column summarizes an example of the collaborative process of app guide development in a publicly funded mental health service context and highlights opportunities and barriers identified through the process.
26.
Mehrabadi, Milad Asgari; Dutt, Nikil; Rahmani, Amir M
The causality inference of public interest in restaurants and bars on daily COVID-19 cases in the United States: Google Trends analysis Journal Article
In: JMIR public health and surveillance, 7 (4), pp. e22880, 2021.
Abstract | Links | BibTeX | Tags:
@article{mehrabadi2021causality,
title = {The causality inference of public interest in restaurants and bars on daily COVID-19 cases in the United States: Google Trends analysis},
author = {Milad Asgari Mehrabadi and Nikil Dutt and Amir M Rahmani},
url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8025919/},
doi = {10.2196/22880},
year = {2021},
date = {2021-04-01},
urldate = {2021-04-01},
journal = {JMIR public health and surveillance},
volume = {7},
number = {4},
pages = {e22880},
publisher = {JMIR Publications Inc., Toronto, Canada},
abstract = {Background: The COVID-19 pandemic has affected virtually every region in the world. At the time of this study, the number of daily new cases in the United States was greater than that in any other country, and the trend was increasing in most states. Google Trends provides data regarding public interest in various topics during different periods. Analyzing these trends using data mining methods may provide useful insights and observations regarding the COVID-19 outbreak.
Objective: The objective of this study is to consider the predictive ability of different search terms not directly related to COVID-19 with regard to the increase of daily cases in the United States. In particular, we are concerned with searches related to dine-in restaurants and bars. Data were obtained from the Google Trends application programming interface and the COVID-19 Tracking Project.
Methods: To test the causation of one time series on another, we used the Granger causality test. We considered the causation of two different search query trends related to dine-in restaurants and bars on daily positive cases in the US states and territories with the 10 highest and 10 lowest numbers of daily new cases of COVID-19. In addition, we used Pearson correlations to measure the linear relationships between different trends.
Results: Our results showed that for states and territories with higher numbers of daily cases, the historical trends in search queries related to bars and restaurants, which mainly occurred after reopening, significantly affected the number of daily new cases on average. California, for example, showed the most searches for restaurants on June 7, 2020; this affected the number of new cases within two weeks after the peak, with a P value of .004 for the Granger causality test.
Conclusions: Although a limited number of search queries were considered, Google search trends for restaurants and bars showed a significant effect on daily new cases in US states and territories with higher numbers of daily new cases. We showed that these influential search trends can be used to provide additional information for prediction tasks regarding new cases in each region. These predictions can help health care leaders manage and control the impact of the COVID-19 outbreak on society and prepare for its outcomes.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Background: The COVID-19 pandemic has affected virtually every region in the world. At the time of this study, the number of daily new cases in the United States was greater than that in any other country, and the trend was increasing in most states. Google Trends provides data regarding public interest in various topics during different periods. Analyzing these trends using data mining methods may provide useful insights and observations regarding the COVID-19 outbreak.
Objective: The objective of this study is to consider the predictive ability of different search terms not directly related to COVID-19 with regard to the increase of daily cases in the United States. In particular, we are concerned with searches related to dine-in restaurants and bars. Data were obtained from the Google Trends application programming interface and the COVID-19 Tracking Project.
Methods: To test the causation of one time series on another, we used the Granger causality test. We considered the causation of two different search query trends related to dine-in restaurants and bars on daily positive cases in the US states and territories with the 10 highest and 10 lowest numbers of daily new cases of COVID-19. In addition, we used Pearson correlations to measure the linear relationships between different trends.
Results: Our results showed that for states and territories with higher numbers of daily cases, the historical trends in search queries related to bars and restaurants, which mainly occurred after reopening, significantly affected the number of daily new cases on average. California, for example, showed the most searches for restaurants on June 7, 2020; this affected the number of new cases within two weeks after the peak, with a P value of .004 for the Granger causality test.
Conclusions: Although a limited number of search queries were considered, Google search trends for restaurants and bars showed a significant effect on daily new cases in US states and territories with higher numbers of daily new cases. We showed that these influential search trends can be used to provide additional information for prediction tasks regarding new cases in each region. These predictions can help health care leaders manage and control the impact of the COVID-19 outbreak on society and prepare for its outcomes.
Objective: The objective of this study is to consider the predictive ability of different search terms not directly related to COVID-19 with regard to the increase of daily cases in the United States. In particular, we are concerned with searches related to dine-in restaurants and bars. Data were obtained from the Google Trends application programming interface and the COVID-19 Tracking Project.
Methods: To test the causation of one time series on another, we used the Granger causality test. We considered the causation of two different search query trends related to dine-in restaurants and bars on daily positive cases in the US states and territories with the 10 highest and 10 lowest numbers of daily new cases of COVID-19. In addition, we used Pearson correlations to measure the linear relationships between different trends.
Results: Our results showed that for states and territories with higher numbers of daily cases, the historical trends in search queries related to bars and restaurants, which mainly occurred after reopening, significantly affected the number of daily new cases on average. California, for example, showed the most searches for restaurants on June 7, 2020; this affected the number of new cases within two weeks after the peak, with a P value of .004 for the Granger causality test.
Conclusions: Although a limited number of search queries were considered, Google search trends for restaurants and bars showed a significant effect on daily new cases in US states and territories with higher numbers of daily new cases. We showed that these influential search trends can be used to provide additional information for prediction tasks regarding new cases in each region. These predictions can help health care leaders manage and control the impact of the COVID-19 outbreak on society and prepare for its outcomes.
25.
Rodrigues, Sarah M; Kanduri, Anil; Nyamathi, Adeline M; Dutt, Nikil; Khargonekar, Pramod P; Rahmani, Amir M
In: JMIR Formative Research, 6 (4), pp. e29535, 2021.
Abstract | Links | BibTeX | Tags:
@article{rodrigues2021digital,
title = {Digital Health-Enabled Community-Centered Care (D-CCC): A Scalable Model to Empower Future Community Health Workers utilizing Human-in-the-Loop AI},
author = {Sarah M Rodrigues and Anil Kanduri and Adeline M Nyamathi and Nikil Dutt and Pramod P Khargonekar and Amir M Rahmani},
url = {https://pubmed.ncbi.nlm.nih.gov/35384853/},
doi = {10.2196/29535},
year = {2021},
date = {2021-04-01},
urldate = {2021-01-01},
journal = {JMIR Formative Research},
volume = {6},
number = {4},
pages = {e29535},
publisher = {Cold Spring Harbor Laboratory Press},
abstract = {Digital health-enabled community-centered care (D-CCC) represents a pioneering vision for the future of community-centered care. D-CCC aims to support and amplify the digital footprint of community health workers through a novel artificial intelligence-enabled closed-loop digital health platform designed for, and with, community health workers. By focusing digitalization at the level of the community health worker, D-CCC enables more timely, supported, and individualized community health worker-delivered interventions. D-CCC has the potential to move community-centered care into an expanded, digitally interconnected, and collaborative community-centered health and social care ecosystem of the future, grounded within a robust and digitally empowered community health workforce.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Digital health-enabled community-centered care (D-CCC) represents a pioneering vision for the future of community-centered care. D-CCC aims to support and amplify the digital footprint of community health workers through a novel artificial intelligence-enabled closed-loop digital health platform designed for, and with, community health workers. By focusing digitalization at the level of the community health worker, D-CCC enables more timely, supported, and individualized community health worker-delivered interventions. D-CCC has the potential to move community-centered care into an expanded, digitally interconnected, and collaborative community-centered health and social care ecosystem of the future, grounded within a robust and digitally empowered community health workforce.
24.
Vo, Khuong; Naeini, Emad Kasaeyan; Naderi, Amir; Jilani, Daniel; Rahmani, Amir M; Dutt, Nikil; Cao, Hung
P2E-WGAN: ECG waveform synthesis from PPG with conditional wasserstein generative adversarial networks Inproceedings
In: Proceedings of the 36th Annual ACM Symposium on Applied Computing, pp. 1030–1036, 2021.
Abstract | Links | BibTeX | Tags:
@inproceedings{vo2021p2e,
title = {P2E-WGAN: ECG waveform synthesis from PPG with conditional wasserstein generative adversarial networks},
author = {Khuong Vo and Emad Kasaeyan Naeini and Amir Naderi and Daniel Jilani and Amir M Rahmani and Nikil Dutt and Hung Cao},
url = {https://dl.acm.org/doi/10.1145/3412841.3441979},
doi = {10.1145/3412841.3441979},
year = {2021},
date = {2021-04-01},
urldate = {2021-01-01},
booktitle = {Proceedings of the 36th Annual ACM Symposium on Applied Computing},
pages = {1030--1036},
abstract = {Electrocardiogram (ECG) is routinely used to identify key cardiac events such as changes in ECG intervals (PR, ST, QT, etc.), as well as capture critical vital signs such as heart rate (HR) and heart rate variability (HRV). The gold standard ECG requires clinical measurement, limiting the ability to capture ECG in everyday settings. Photoplethysmography (PPG) offers an out-of-clinic alternative for non-invasive, low-cost optical capture of cardiac physiological measurement in everyday settings, and is increasingly used for health monitoring in many clinical and commercial wearable devices. Although ECG and PPG are highly correlated, PPG does not provide much information for clinical diagnosis. Recent work has applied machine learning algorithms to generate ECG signals from PPG, but requires expert domain knowledge and heavy feature crafting to achieve good accuracy. We propose P2E-WGAN: a pure end-to-end, generalizable deep learning model using a conditional Wasserstein generative adversarial network to synthesize ECG waveforms from PPG. Our generative model is capable of augmenting the training data to alleviate the data-hungry problem of machine learning methods. Our model trained in the subject independent mode can achieve the average root mean square error of 0.162, Fréchet distance of 0.375, and Pearson's correlation of 0.835 on a normalized real-world dataset, demonstrating the effectiveness of our approach.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Electrocardiogram (ECG) is routinely used to identify key cardiac events such as changes in ECG intervals (PR, ST, QT, etc.), as well as capture critical vital signs such as heart rate (HR) and heart rate variability (HRV). The gold standard ECG requires clinical measurement, limiting the ability to capture ECG in everyday settings. Photoplethysmography (PPG) offers an out-of-clinic alternative for non-invasive, low-cost optical capture of cardiac physiological measurement in everyday settings, and is increasingly used for health monitoring in many clinical and commercial wearable devices. Although ECG and PPG are highly correlated, PPG does not provide much information for clinical diagnosis. Recent work has applied machine learning algorithms to generate ECG signals from PPG, but requires expert domain knowledge and heavy feature crafting to achieve good accuracy. We propose P2E-WGAN: a pure end-to-end, generalizable deep learning model using a conditional Wasserstein generative adversarial network to synthesize ECG waveforms from PPG. Our generative model is capable of augmenting the training data to alleviate the data-hungry problem of machine learning methods. Our model trained in the subject independent mode can achieve the average root mean square error of 0.162, Fréchet distance of 0.375, and Pearson's correlation of 0.835 on a normalized real-world dataset, demonstrating the effectiveness of our approach.
23.
Woodworth, Amanda; Schneider, Margaret
Critical Evaluation of the Case for Pausing California's School-based Fitness Testing Journal Article
In: Health behavior and policy review, 8 (2), pp. 168–183, 2021.
Abstract | Links | BibTeX | Tags:
@article{woodworth2021critical,
title = {Critical Evaluation of the Case for Pausing California's School-based Fitness Testing},
author = {Amanda Woodworth and Margaret Schneider},
url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8130883/},
doi = {10.14485/HBPR.8.2.7},
year = {2021},
date = {2021-03-01},
urldate = {2021-01-01},
journal = {Health behavior and policy review},
volume = {8},
number = {2},
pages = {168--183},
publisher = {Paris Scholar Publishing Ltd.},
abstract = {Objective: We undertook a literature review to evaluate the evidence for an association among school-based fitness testing and bullying, weight-based teasing (WBT), and/or gender discrimination.
Methods: We searched the peer-reviewed literature using PubMed, ERIC and GOOGLE Scholar to identify articles related to school-based physical fitness testing (K-12) on the one hand and bullying, WBT, and/or gender discrimination on the other.
Results: We identified 12 studies on the impact of school-based physical fitness testing (PFT) on bullying and WBT. These studies do not support the assertion that PFT places students at elevated risk for bullying and/or WBT as compared to other school settings. There is a dearth of studies investigating an association between PFT and gender discrimination.
Conclusions: The concerns about PFT as a widespread cause of bullying and WBT are not supported by the evidence. It is likely that school climate is a stronger determinant overall of these negative student interactions and that more rigorous teacher training would ameliorate student concerns about fitness testing. Nevertheless, more rigorous research is warranted to determine with confidence that PFT does not elevate students' risks for bullying and WBT and to examine the risks for students with non-binary gender.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Objective: We undertook a literature review to evaluate the evidence for an association among school-based fitness testing and bullying, weight-based teasing (WBT), and/or gender discrimination.
Methods: We searched the peer-reviewed literature using PubMed, ERIC and GOOGLE Scholar to identify articles related to school-based physical fitness testing (K-12) on the one hand and bullying, WBT, and/or gender discrimination on the other.
Results: We identified 12 studies on the impact of school-based physical fitness testing (PFT) on bullying and WBT. These studies do not support the assertion that PFT places students at elevated risk for bullying and/or WBT as compared to other school settings. There is a dearth of studies investigating an association between PFT and gender discrimination.
Conclusions: The concerns about PFT as a widespread cause of bullying and WBT are not supported by the evidence. It is likely that school climate is a stronger determinant overall of these negative student interactions and that more rigorous teacher training would ameliorate student concerns about fitness testing. Nevertheless, more rigorous research is warranted to determine with confidence that PFT does not elevate students' risks for bullying and WBT and to examine the risks for students with non-binary gender.
Methods: We searched the peer-reviewed literature using PubMed, ERIC and GOOGLE Scholar to identify articles related to school-based physical fitness testing (K-12) on the one hand and bullying, WBT, and/or gender discrimination on the other.
Results: We identified 12 studies on the impact of school-based physical fitness testing (PFT) on bullying and WBT. These studies do not support the assertion that PFT places students at elevated risk for bullying and/or WBT as compared to other school settings. There is a dearth of studies investigating an association between PFT and gender discrimination.
Conclusions: The concerns about PFT as a widespread cause of bullying and WBT are not supported by the evidence. It is likely that school climate is a stronger determinant overall of these negative student interactions and that more rigorous teacher training would ameliorate student concerns about fitness testing. Nevertheless, more rigorous research is warranted to determine with confidence that PFT does not elevate students' risks for bullying and WBT and to examine the risks for students with non-binary gender.
22.
Sorkin, Dara H; Janio, Emily A; Eikey, Elizabeth V; Schneider, Margaret; Davis, Katelyn; Schueller, Stephen M; Stadnick, Nicole A; Zheng, Kai; Neary, Martha; Safani, David; Mukamel, Dana B
Rise in use of digital mental health tools and Technologies in the United States during the COVID-19 pandemic: survey study Journal Article
In: Journal of medical Internet research, 23 (4), pp. e26994, 2021.
Abstract | Links | BibTeX | Tags:
@article{sorkin2021rise,
title = {Rise in use of digital mental health tools and Technologies in the United States during the COVID-19 pandemic: survey study},
author = {Dara H Sorkin and Emily A Janio and Elizabeth V Eikey and Margaret Schneider and Katelyn Davis and Stephen M Schueller and Nicole A Stadnick and Kai Zheng and Martha Neary and David Safani and Dana B Mukamel},
url = {https://pubmed.ncbi.nlm.nih.gov/33822737/},
doi = {10.2196/26994},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {Journal of medical Internet research},
volume = {23},
number = {4},
pages = {e26994},
publisher = {JMIR Publications Inc., Toronto, Canada},
abstract = {Background: Accompanying the rising rates of reported mental distress during the COVID-19 pandemic has been a reported increase in the use of digital technologies to manage health generally, and mental health more specifically.
Objective: The objective of this study was to systematically examine whether there was a COVID-19 pandemic-related increase in the self-reported use of digital mental health tools and other technologies to manage mental health.
Methods: We analyzed results from a survey of 5907 individuals in the United States using Amazon Mechanical Turk (MTurk); the survey was administered during 4 week-long periods in 2020 and survey respondents were from all 50 states and Washington DC. The first set of analyses employed two different logistic regression models to estimate the likelihood of having symptoms indicative of clinical depression and anxiety, respectively, as a function of the rate of COVID-19 cases per 10 people and survey time point. The second set employed seven different logistic regression models to estimate the likelihood of using seven different types of digital mental health tools and other technologies to manage one's mental health, as a function of symptoms indicative of clinical depression and anxiety, rate of COVID-19 cases per 10 people, and survey time point. These models also examined potential interactions between symptoms of clinical depression and anxiety, respectively, and rate of COVID-19 cases. All models controlled for respondent sociodemographic characteristics and state fixed effects.
Results: Higher COVID-19 case rates were associated with a significantly greater likelihood of reporting symptoms of depression (odds ratio [OR] 2.06, 95% CI 1.27-3.35), but not anxiety (OR 1.21, 95% CI 0.77-1.88). Survey time point, a proxy for time, was associated with a greater likelihood of reporting clinically meaningful symptoms of depression and anxiety (OR 1.19, 95% CI 1.12-1.27 and OR 1.12, 95% CI 1.05-1.19, respectively). Reported symptoms of depression and anxiety were associated with a greater likelihood of using each type of technology. Higher COVID-19 case rates were associated with a significantly greater likelihood of using mental health forums, websites, or apps (OR 2.70, 95% CI 1.49-4.88), and other health forums, websites, or apps (OR 2.60, 95% CI 1.55-4.34). Time was associated with increased odds of reported use of mental health forums, websites, or apps (OR 1.20, 95% CI 1.11-1.30), phone-based or text-based crisis lines (OR 1.20, 95% CI 1.10-1.31), and online, computer, or console gaming/video gaming (OR 1.12, 95% CI 1.05-1.19). Interactions between COVID-19 case rate and mental health symptoms were not significantly associated with any of the technology types.
Conclusions: Findings suggested increased use of digital mental health tools and other technologies over time during the early stages of the COVID-19 pandemic. As such, additional effort is urgently needed to consider the quality of these products, either by ensuring users have access to evidence-based and evidence-informed technologies and/or by providing them with the skills to make informed decisions around their potential efficacy.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Background: Accompanying the rising rates of reported mental distress during the COVID-19 pandemic has been a reported increase in the use of digital technologies to manage health generally, and mental health more specifically.
Objective: The objective of this study was to systematically examine whether there was a COVID-19 pandemic-related increase in the self-reported use of digital mental health tools and other technologies to manage mental health.
Methods: We analyzed results from a survey of 5907 individuals in the United States using Amazon Mechanical Turk (MTurk); the survey was administered during 4 week-long periods in 2020 and survey respondents were from all 50 states and Washington DC. The first set of analyses employed two different logistic regression models to estimate the likelihood of having symptoms indicative of clinical depression and anxiety, respectively, as a function of the rate of COVID-19 cases per 10 people and survey time point. The second set employed seven different logistic regression models to estimate the likelihood of using seven different types of digital mental health tools and other technologies to manage one's mental health, as a function of symptoms indicative of clinical depression and anxiety, rate of COVID-19 cases per 10 people, and survey time point. These models also examined potential interactions between symptoms of clinical depression and anxiety, respectively, and rate of COVID-19 cases. All models controlled for respondent sociodemographic characteristics and state fixed effects.
Results: Higher COVID-19 case rates were associated with a significantly greater likelihood of reporting symptoms of depression (odds ratio [OR] 2.06, 95% CI 1.27-3.35), but not anxiety (OR 1.21, 95% CI 0.77-1.88). Survey time point, a proxy for time, was associated with a greater likelihood of reporting clinically meaningful symptoms of depression and anxiety (OR 1.19, 95% CI 1.12-1.27 and OR 1.12, 95% CI 1.05-1.19, respectively). Reported symptoms of depression and anxiety were associated with a greater likelihood of using each type of technology. Higher COVID-19 case rates were associated with a significantly greater likelihood of using mental health forums, websites, or apps (OR 2.70, 95% CI 1.49-4.88), and other health forums, websites, or apps (OR 2.60, 95% CI 1.55-4.34). Time was associated with increased odds of reported use of mental health forums, websites, or apps (OR 1.20, 95% CI 1.11-1.30), phone-based or text-based crisis lines (OR 1.20, 95% CI 1.10-1.31), and online, computer, or console gaming/video gaming (OR 1.12, 95% CI 1.05-1.19). Interactions between COVID-19 case rate and mental health symptoms were not significantly associated with any of the technology types.
Conclusions: Findings suggested increased use of digital mental health tools and other technologies over time during the early stages of the COVID-19 pandemic. As such, additional effort is urgently needed to consider the quality of these products, either by ensuring users have access to evidence-based and evidence-informed technologies and/or by providing them with the skills to make informed decisions around their potential efficacy.
Objective: The objective of this study was to systematically examine whether there was a COVID-19 pandemic-related increase in the self-reported use of digital mental health tools and other technologies to manage mental health.
Methods: We analyzed results from a survey of 5907 individuals in the United States using Amazon Mechanical Turk (MTurk); the survey was administered during 4 week-long periods in 2020 and survey respondents were from all 50 states and Washington DC. The first set of analyses employed two different logistic regression models to estimate the likelihood of having symptoms indicative of clinical depression and anxiety, respectively, as a function of the rate of COVID-19 cases per 10 people and survey time point. The second set employed seven different logistic regression models to estimate the likelihood of using seven different types of digital mental health tools and other technologies to manage one's mental health, as a function of symptoms indicative of clinical depression and anxiety, rate of COVID-19 cases per 10 people, and survey time point. These models also examined potential interactions between symptoms of clinical depression and anxiety, respectively, and rate of COVID-19 cases. All models controlled for respondent sociodemographic characteristics and state fixed effects.
Results: Higher COVID-19 case rates were associated with a significantly greater likelihood of reporting symptoms of depression (odds ratio [OR] 2.06, 95% CI 1.27-3.35), but not anxiety (OR 1.21, 95% CI 0.77-1.88). Survey time point, a proxy for time, was associated with a greater likelihood of reporting clinically meaningful symptoms of depression and anxiety (OR 1.19, 95% CI 1.12-1.27 and OR 1.12, 95% CI 1.05-1.19, respectively). Reported symptoms of depression and anxiety were associated with a greater likelihood of using each type of technology. Higher COVID-19 case rates were associated with a significantly greater likelihood of using mental health forums, websites, or apps (OR 2.70, 95% CI 1.49-4.88), and other health forums, websites, or apps (OR 2.60, 95% CI 1.55-4.34). Time was associated with increased odds of reported use of mental health forums, websites, or apps (OR 1.20, 95% CI 1.11-1.30), phone-based or text-based crisis lines (OR 1.20, 95% CI 1.10-1.31), and online, computer, or console gaming/video gaming (OR 1.12, 95% CI 1.05-1.19). Interactions between COVID-19 case rate and mental health symptoms were not significantly associated with any of the technology types.
Conclusions: Findings suggested increased use of digital mental health tools and other technologies over time during the early stages of the COVID-19 pandemic. As such, additional effort is urgently needed to consider the quality of these products, either by ensuring users have access to evidence-based and evidence-informed technologies and/or by providing them with the skills to make informed decisions around their potential efficacy.
21.
Borghouts, Judith; Eikey, Elizabeth; Mark, Gloria; Leon, Cinthia De; Schueller, Stephen M; Schneider, Margaret; Stadnick, Nicole; Zheng, Kai; Mukamel, Dana; Sorkin, Dara H
Barriers to and facilitators of user engagement with digital mental health interventions: systematic review Journal Article
In: Journal of medical Internet research, 23 (3), pp. e24387, 2021.
BibTeX | Tags:
@article{borghouts2021barriers,
title = {Barriers to and facilitators of user engagement with digital mental health interventions: systematic review},
author = {Judith Borghouts and Elizabeth Eikey and Gloria Mark and Cinthia De Leon and Stephen M Schueller and Margaret Schneider and Nicole Stadnick and Kai Zheng and Dana Mukamel and Dara H Sorkin},
year = {2021},
date = {2021-01-01},
journal = {Journal of medical Internet research},
volume = {23},
number = {3},
pages = {e24387},
publisher = {JMIR Publications Inc., Toronto, Canada},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
20.
Donyanavard, Bryan; Rahmani, Amir M; Jantsch, Axel; Mutlu, Onur; Dutt, Nikil
Intelligent management of mobile systems through computational self-awareness Incollection
In: Handbook of Research on Methodologies and Applications of Supercomputing, pp. 41–73, IGI Global, 2021.
BibTeX | Tags:
@incollection{donyanavard2021intelligent,
title = {Intelligent management of mobile systems through computational self-awareness},
author = {Bryan Donyanavard and Amir M Rahmani and Axel Jantsch and Onur Mutlu and Nikil Dutt},
year = {2021},
date = {2021-01-01},
booktitle = {Handbook of Research on Methodologies and Applications of Supercomputing},
pages = {41--73},
publisher = {IGI Global},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
19.
Donyanavard, Bryan; Mück, Tiago; Moazzemi, Kasra; Maity, Biswadip; Melo, Caio Batista; Stewart, Kenneth; Yi, Saehanseul; Rahmani, Amir M; Dutt, Nikil
Reflecting on Self-Aware Systems-on-Chip Incollection
In: A Journey of Embedded and Cyber-Physical Systems, pp. 79–95, Springer, Cham, 2021.
Abstract | Links | BibTeX | Tags:
@incollection{donyanavard2021reflecting,
title = {Reflecting on Self-Aware Systems-on-Chip},
author = {Bryan Donyanavard and Tiago Mück and Kasra Moazzemi and Biswadip Maity and Caio Batista Melo and Kenneth Stewart and Saehanseul Yi and Amir M Rahmani and Nikil Dutt},
url = {https://link.springer.com/chapter/10.1007/978-3-030-47487-4_6},
doi = {10.1007/978-3-030-47487-4_6},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
booktitle = {A Journey of Embedded and Cyber-Physical Systems},
pages = {79--95},
publisher = {Springer, Cham},
series = {A Journey of Embedded and Cyber-Physical Systems},
abstract = {In this chapter, we explore adaptive resource management techniques for cyber-physical systems-on-chip that employ principles of computational self-awareness to varying degrees, specifically reflection. By supporting various self-X properties, systems gain the ability to reason about runtime configuration decisions by considering the significance of competing objectives, user requirements, and operating conditions, while executing unpredictable workloads.},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
In this chapter, we explore adaptive resource management techniques for cyber-physical systems-on-chip that employ principles of computational self-awareness to varying degrees, specifically reflection. By supporting various self-X properties, systems gain the ability to reason about runtime configuration decisions by considering the significance of competing objectives, user requirements, and operating conditions, while executing unpredictable workloads.
18.
Monazzah, Amir Mahdi Hosseini; Rahmani, Amir M; Miele, Antonio; Dutt, Nikil
Exploiting Memory Resilience for Emerging Technologies: An Energy-Aware Resilience Exemplar for STT-RAM Memories Book Chapter
In: pp. 505, Springer, Cham, 2021.
Abstract | Links | BibTeX | Tags:
@inbook{Monazzah2021exploiting,
title = {Exploiting Memory Resilience for Emerging Technologies: An Energy-Aware Resilience Exemplar for STT-RAM Memories},
author = {Amir Mahdi Hosseini Monazzah and Amir M Rahmani and Antonio Miele and Nikil Dutt},
url = {https://link.springer.com/chapter/10.1007/978-3-030-52017-5_21},
doi = {10.1007/978-3-030-52017-5_21},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {Dependable Embedded Systems},
pages = {505},
publisher = {Springer, Cham},
series = {Dependable Embedded Systems},
abstract = {Due to the consistent pressing quest of larger on-chip memories and caches of multicore and manycore architectures, Spin Transfer Torque Magnetic RAM (STT-MRAM or STT-RAM) has been proposed as a promising technology to replace classical SRAMs in near-future devices. Main advantages of STT-RAMs are a considerably higher transistor density and a negligible leakage power compared with SRAM technology. However, the drawback of this technology is the high probability of errors occurring especially in write operations. Such errors are asymmetric and transition-dependent, where 0 → 1 is the most critical one, and is high subjected to the amount and current (voltage) supplied to the memory during the write operation. As a consequence, STT-RAMs present an intrinsic trade-off between energy consumption vs. reliability that needs to be properly tuned w.r.t. the currently running application and its reliability requirement. This chapter proposes FlexRel, an energy-aware reliability improvement architectural scheme for STT-RAM cache memories. FlexRel considers a memory architecture provided with Error Correction Codes (ECCs) and a custom current regulator for the various cache ways and conducts a trade-off between reliability and energy consumption. FlexRel cache controller dynamically profiles the number of 0 → 1 transitions of each individual bit write operation in a cache block and based on that selects the most-suitable cache way and current level to guarantee the necessary error rate threshold (in terms of occurred write errors) while minimizing the energy consumption. We experimentally evaluated the efficiency of FlexRel against the most efficient uniform protection scheme from reliability, energy, area, and performance perspectives. Experimental simulations performed by using gem5 has demonstrated that while FlexRel satisfies the given error rate threshold, it delivers up to 13.2% energy saving. From the area footprint perspective, FlexRel delivers up to 7.9% cache ways’ area saving. Furthermore, the performance overhead of the FlexRel algorithm which changes the traffic patterns of the cache ways during the executions is 1.7%, on average.},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
Due to the consistent pressing quest of larger on-chip memories and caches of multicore and manycore architectures, Spin Transfer Torque Magnetic RAM (STT-MRAM or STT-RAM) has been proposed as a promising technology to replace classical SRAMs in near-future devices. Main advantages of STT-RAMs are a considerably higher transistor density and a negligible leakage power compared with SRAM technology. However, the drawback of this technology is the high probability of errors occurring especially in write operations. Such errors are asymmetric and transition-dependent, where 0 → 1 is the most critical one, and is high subjected to the amount and current (voltage) supplied to the memory during the write operation. As a consequence, STT-RAMs present an intrinsic trade-off between energy consumption vs. reliability that needs to be properly tuned w.r.t. the currently running application and its reliability requirement. This chapter proposes FlexRel, an energy-aware reliability improvement architectural scheme for STT-RAM cache memories. FlexRel considers a memory architecture provided with Error Correction Codes (ECCs) and a custom current regulator for the various cache ways and conducts a trade-off between reliability and energy consumption. FlexRel cache controller dynamically profiles the number of 0 → 1 transitions of each individual bit write operation in a cache block and based on that selects the most-suitable cache way and current level to guarantee the necessary error rate threshold (in terms of occurred write errors) while minimizing the energy consumption. We experimentally evaluated the efficiency of FlexRel against the most efficient uniform protection scheme from reliability, energy, area, and performance perspectives. Experimental simulations performed by using gem5 has demonstrated that while FlexRel satisfies the given error rate threshold, it delivers up to 13.2% energy saving. From the area footprint perspective, FlexRel delivers up to 7.9% cache ways’ area saving. Furthermore, the performance overhead of the FlexRel algorithm which changes the traffic patterns of the cache ways during the executions is 1.7%, on average.
17.
Kerr, Margaret L; Rasmussen, Hannah F; Smiley, Patricia A; Buttitta, Katherine V; Borelli, Jessica L
The development of toddlers’ emotion regulation within the family system: associations with observed parent-child synchrony and interparental relationship satisfaction Journal Article
In: Early Childhood Research Quarterly, 57 , pp. 215–227, 2021.
BibTeX | Tags:
@article{kerr2021development,
title = {The development of toddlers’ emotion regulation within the family system: associations with observed parent-child synchrony and interparental relationship satisfaction},
author = {Margaret L Kerr and Hannah F Rasmussen and Patricia A Smiley and Katherine V Buttitta and Jessica L Borelli},
year = {2021},
date = {2021-01-01},
journal = {Early Childhood Research Quarterly},
volume = {57},
pages = {215--227},
publisher = {Elsevier},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
16.
Bizzi, Fabiola; Charpentier-Mora, Simone; Cavanna, Donatella; Borelli, Jessica L; Ensink, Karin
Testing Children’s Mentalizing in Middle Childhood: Adopting the Child and Adolescent Reflective Functioning Scale with Clinical and Community Children Journal Article
In: Journal of Child and Family Studies, pp. 1–14, 2021.
BibTeX | Tags:
@article{bizzi2021testing,
title = {Testing Children’s Mentalizing in Middle Childhood: Adopting the Child and Adolescent Reflective Functioning Scale with Clinical and Community Children},
author = {Fabiola Bizzi and Simone Charpentier-Mora and Donatella Cavanna and Jessica L Borelli and Karin Ensink},
year = {2021},
date = {2021-01-01},
journal = {Journal of Child and Family Studies},
pages = {1--14},
publisher = {Springer},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
15.
Pereira, Ariel S; Azhari, Atiqah; Hong, Chloe A; Gaskin, Gerin E; Borelli, Jessica L; Esposito, Gianluca
Savouring as an Intervention to Decrease Negative Affect in Anxious Mothers of Children with Autism and Neurotypical Children Journal Article
In: Brain Sciences, 11 (5), pp. 652, 2021.
BibTeX | Tags:
@article{pereira2021savouring,
title = {Savouring as an Intervention to Decrease Negative Affect in Anxious Mothers of Children with Autism and Neurotypical Children},
author = {Ariel S Pereira and Atiqah Azhari and Chloe A Hong and Gerin E Gaskin and Jessica L Borelli and Gianluca Esposito},
year = {2021},
date = {2021-01-01},
journal = {Brain Sciences},
volume = {11},
number = {5},
pages = {652},
publisher = {Multidisciplinary Digital Publishing Institute},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
14.
Gruber, June; Mendle, Jane; Lindquist, Kristen A; Schmader, Toni; Clark, Lee Anna; Bliss-Moreau, Eliza; Akinola, Modupe; Atlas, Lauren; Barch, Deanna M; Barrett, Lisa Feldman; Borelli, Jessica; others,
The future of women in psychological science Journal Article
In: Perspectives on Psychological Science, 16 (3), pp. 483–516, 2021.
BibTeX | Tags:
@article{gruber2021future,
title = {The future of women in psychological science},
author = {June Gruber and Jane Mendle and Kristen A Lindquist and Toni Schmader and Lee Anna Clark and Eliza Bliss-Moreau and Modupe Akinola and Lauren Atlas and Deanna M Barch and Lisa Feldman Barrett and Jessica Borelli and others},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {Perspectives on Psychological Science},
volume = {16},
number = {3},
pages = {483--516},
publisher = {SAGE Publications Sage CA: Los Angeles, CA},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
13.
Borelli, Jessica L; Gaskin, Gerin; Smiley, Patricia; Chung, Debbie; Shahar, Ben; Bosmans, Guy
Multisystem physiological reactivity during help-seeking for attachment needs in school-aged children: differences as a function of attachment Journal Article
In: Attachment & Human Development, pp. 1–15, 2021.
BibTeX | Tags:
@article{borelli2021multisystem,
title = {Multisystem physiological reactivity during help-seeking for attachment needs in school-aged children: differences as a function of attachment},
author = {Jessica L Borelli and Gerin Gaskin and Patricia Smiley and Debbie Chung and Ben Shahar and Guy Bosmans},
year = {2021},
date = {2021-01-01},
journal = {Attachment & Human Development},
pages = {1--15},
publisher = {Taylor & Francis},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
12.
Yunusova, Asal; Lai, Jocelyn; Rivera, Alexander P; Hu, Sirui; Labbaf, Sina; Rahmani, Amir M; Dutt, Nikil; Jain, Ramesh C; Borelli, Jessica L
Assessing the Mental Health of Emerging Adults Through a Mental Health App: Protocol for a Prospective Pilot Study Journal Article
In: JMIR Research Protocols, 10 (3), pp. e25775, 2021.
Abstract | Links | BibTeX | Tags: MHN
@article{yunusova2021assessing,
title = {Assessing the Mental Health of Emerging Adults Through a Mental Health App: Protocol for a Prospective Pilot Study},
author = {Asal Yunusova and Jocelyn Lai and Alexander P Rivera and Sirui Hu and Sina Labbaf and Amir M Rahmani and Nikil Dutt and Ramesh C Jain and Jessica L Borelli},
url = {https://pubmed.ncbi.nlm.nih.gov/33513124/},
doi = {10.2196/25775},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {JMIR Research Protocols},
volume = {10},
number = {3},
pages = {e25775},
publisher = {JMIR Publications Inc., Toronto, Canada},
abstract = {Background: Individuals can experience different manifestations of the same psychological disorder. This underscores the need for a personalized model approach in the study of psychopathology. Emerging adulthood is a developmental phase wherein individuals are especially vulnerable to psychopathology. Given their exposure to repeated stressors and disruptions in routine, the emerging adult population is worthy of investigation.
Objective: In our prospective study, we aim to conduct multimodal assessments to determine the feasibility of an individualized approach for understanding the contextual factors of changes in daily affect, sleep, physiology, and activity. In other words, we aim to use event mining to predict changes in mental health.
Methods: We expect to have a final sample size of 20 participants. Recruited participants will be monitored for a period of time (ie, between 3 and 12 months). Participants will download the Personicle app on their smartphone to track their activities (eg, home events and cycling). They will also be given wearable sensor devices (ie, devices that monitor sleep, physiology, and physical activity), which are to be worn continuously. Participants will be asked to report on their daily moods and provide open-ended text responses on a weekly basis. Participants will be given a battery of questionnaires every 3 months.
Results: Our study has been approved by an institutional review board. The study is currently in the data collection phase. Due to the COVID-19 pandemic, the study was adjusted to allow for remote data collection and COVID-19-related stress assessments.
Conclusions: Our study will help advance research on individualized approaches to understanding health and well-being through multimodal systems. Our study will also demonstrate the benefit of using individualized approaches to study interrelations among stress, social relationships, technology, and mental health.},
keywords = {MHN},
pubstate = {published},
tppubtype = {article}
}
Background: Individuals can experience different manifestations of the same psychological disorder. This underscores the need for a personalized model approach in the study of psychopathology. Emerging adulthood is a developmental phase wherein individuals are especially vulnerable to psychopathology. Given their exposure to repeated stressors and disruptions in routine, the emerging adult population is worthy of investigation.
Objective: In our prospective study, we aim to conduct multimodal assessments to determine the feasibility of an individualized approach for understanding the contextual factors of changes in daily affect, sleep, physiology, and activity. In other words, we aim to use event mining to predict changes in mental health.
Methods: We expect to have a final sample size of 20 participants. Recruited participants will be monitored for a period of time (ie, between 3 and 12 months). Participants will download the Personicle app on their smartphone to track their activities (eg, home events and cycling). They will also be given wearable sensor devices (ie, devices that monitor sleep, physiology, and physical activity), which are to be worn continuously. Participants will be asked to report on their daily moods and provide open-ended text responses on a weekly basis. Participants will be given a battery of questionnaires every 3 months.
Results: Our study has been approved by an institutional review board. The study is currently in the data collection phase. Due to the COVID-19 pandemic, the study was adjusted to allow for remote data collection and COVID-19-related stress assessments.
Conclusions: Our study will help advance research on individualized approaches to understanding health and well-being through multimodal systems. Our study will also demonstrate the benefit of using individualized approaches to study interrelations among stress, social relationships, technology, and mental health.
Objective: In our prospective study, we aim to conduct multimodal assessments to determine the feasibility of an individualized approach for understanding the contextual factors of changes in daily affect, sleep, physiology, and activity. In other words, we aim to use event mining to predict changes in mental health.
Methods: We expect to have a final sample size of 20 participants. Recruited participants will be monitored for a period of time (ie, between 3 and 12 months). Participants will download the Personicle app on their smartphone to track their activities (eg, home events and cycling). They will also be given wearable sensor devices (ie, devices that monitor sleep, physiology, and physical activity), which are to be worn continuously. Participants will be asked to report on their daily moods and provide open-ended text responses on a weekly basis. Participants will be given a battery of questionnaires every 3 months.
Results: Our study has been approved by an institutional review board. The study is currently in the data collection phase. Due to the COVID-19 pandemic, the study was adjusted to allow for remote data collection and COVID-19-related stress assessments.
Conclusions: Our study will help advance research on individualized approaches to understanding health and well-being through multimodal systems. Our study will also demonstrate the benefit of using individualized approaches to study interrelations among stress, social relationships, technology, and mental health.
11.
Lai, Jocelyn; Rahmani, Amir; Yunusova, Asal; Rivera, Alexander P; Labbaf, Sina; Hu, Sirui; Dutt, Nikil; Jain, Ramesh; Borelli, Jessica L; others,
Using Multimodal Assessments to Capture Personalized Contexts of College Student Well-being in 2020: Case Study Journal Article
In: JMIR formative research, 5 (5), pp. e26186, 2021.
@article{lai2021using,
title = {Using Multimodal Assessments to Capture Personalized Contexts of College Student Well-being in 2020: Case Study},
author = {Jocelyn Lai and Amir Rahmani and Asal Yunusova and Alexander P Rivera and Sina Labbaf and Sirui Hu and Nikil Dutt and Ramesh Jain and Jessica L Borelli and others},
year = {2021},
date = {2021-01-01},
journal = {JMIR formative research},
volume = {5},
number = {5},
pages = {e26186},
publisher = {JMIR Publications Inc., Toronto, Canada},
keywords = {MHN},
pubstate = {published},
tppubtype = {article}
}
10.
Naeini, Emad Kasaeyan; Subramanian, Ajan; Calderon, Michael-David; Zheng, Kai; Dutt, Nikil; Liljeberg, Pasi; Salantera, Sanna; Nelson, Ariana M; Rahmani, Amir M
Pain Recognition With Electrocardiographic Features in Postoperative Patients: Method Validation Study Journal Article
In: Journal of Medical Internet Research, 23 (5), pp. e25079, 2021.
Abstract | Links | BibTeX | Tags:
@article{naeini2021painb,
title = {Pain Recognition With Electrocardiographic Features in Postoperative Patients: Method Validation Study},
author = {Emad Kasaeyan Naeini and Ajan Subramanian and Michael-David Calderon and Kai Zheng and Nikil Dutt and Pasi Liljeberg and Sanna Salantera and Ariana M Nelson and Amir M Rahmani},
url = {https://pubmed.ncbi.nlm.nih.gov/34047710/},
doi = {10.2196/25079},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {Journal of Medical Internet Research},
volume = {23},
number = {5},
pages = {e25079},
publisher = {JMIR Publications Inc., Toronto, Canada},
abstract = {Background: There is a strong demand for an accurate and objective means of assessing acute pain among hospitalized patients to help clinicians provide pain medications at a proper dosage and in a timely manner. Heart rate variability (HRV) comprises changes in the time intervals between consecutive heartbeats, which can be measured through acquisition and interpretation of electrocardiography (ECG) captured from bedside monitors or wearable devices. As increased sympathetic activity affects the HRV, an index of autonomic regulation of heart rate, ultra-short-term HRV analysis can provide a reliable source of information for acute pain monitoring. In this study, widely used HRV time and frequency domain measurements are used in acute pain assessments among postoperative patients. The existing approaches have only focused on stimulated pain in healthy subjects, whereas, to the best of our knowledge, there is no work in the literature building models using real pain data and on postoperative patients.
Objective: The objective of our study was to develop and evaluate an automatic and adaptable pain assessment algorithm based on ECG features for assessing acute pain in postoperative patients likely experiencing mild to moderate pain.
Methods: The study used a prospective observational design. The sample consisted of 25 patient participants aged 18 to 65 years. In part 1 of the study, a transcutaneous electrical nerve stimulation unit was employed to obtain baseline discomfort thresholds for the patients. In part 2, a multichannel biosignal acquisition device was used as patients were engaging in non-noxious activities. At all times, pain intensity was measured using patient self-reports based on the Numerical Rating Scale. A weak supervision framework was inherited for rapid training data creation. The collected labels were then transformed from 11 intensity levels to 5 intensity levels. Prediction models were developed using 5 different machine learning methods. Mean prediction accuracy was calculated using leave-one-out cross-validation. We compared the performance of these models with the results from a previously published research study.
Results: Five different machine learning algorithms were applied to perform a binary classification of baseline (BL) versus 4 distinct pain levels (PL1 through PL4). The highest validation accuracy using 3 time domain HRV features from a BioVid research paper for baseline versus any other pain level was achieved by support vector machine (SVM) with 62.72% (BL vs PL4) to 84.14% (BL vs PL2). Similar results were achieved for the top 8 features based on the Gini index using the SVM method, with an accuracy ranging from 63.86% (BL vs PL4) to 84.79% (BL vs PL2).
Conclusions: We propose a novel pain assessment method for postoperative patients using ECG signal. Weak supervision applied for labeling and feature extraction improves the robustness of the approach. Our results show the viability of using a machine learning algorithm to accurately and objectively assess acute pain among hospitalized patients.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Background: There is a strong demand for an accurate and objective means of assessing acute pain among hospitalized patients to help clinicians provide pain medications at a proper dosage and in a timely manner. Heart rate variability (HRV) comprises changes in the time intervals between consecutive heartbeats, which can be measured through acquisition and interpretation of electrocardiography (ECG) captured from bedside monitors or wearable devices. As increased sympathetic activity affects the HRV, an index of autonomic regulation of heart rate, ultra-short-term HRV analysis can provide a reliable source of information for acute pain monitoring. In this study, widely used HRV time and frequency domain measurements are used in acute pain assessments among postoperative patients. The existing approaches have only focused on stimulated pain in healthy subjects, whereas, to the best of our knowledge, there is no work in the literature building models using real pain data and on postoperative patients.
Objective: The objective of our study was to develop and evaluate an automatic and adaptable pain assessment algorithm based on ECG features for assessing acute pain in postoperative patients likely experiencing mild to moderate pain.
Methods: The study used a prospective observational design. The sample consisted of 25 patient participants aged 18 to 65 years. In part 1 of the study, a transcutaneous electrical nerve stimulation unit was employed to obtain baseline discomfort thresholds for the patients. In part 2, a multichannel biosignal acquisition device was used as patients were engaging in non-noxious activities. At all times, pain intensity was measured using patient self-reports based on the Numerical Rating Scale. A weak supervision framework was inherited for rapid training data creation. The collected labels were then transformed from 11 intensity levels to 5 intensity levels. Prediction models were developed using 5 different machine learning methods. Mean prediction accuracy was calculated using leave-one-out cross-validation. We compared the performance of these models with the results from a previously published research study.
Results: Five different machine learning algorithms were applied to perform a binary classification of baseline (BL) versus 4 distinct pain levels (PL1 through PL4). The highest validation accuracy using 3 time domain HRV features from a BioVid research paper for baseline versus any other pain level was achieved by support vector machine (SVM) with 62.72% (BL vs PL4) to 84.14% (BL vs PL2). Similar results were achieved for the top 8 features based on the Gini index using the SVM method, with an accuracy ranging from 63.86% (BL vs PL4) to 84.79% (BL vs PL2).
Conclusions: We propose a novel pain assessment method for postoperative patients using ECG signal. Weak supervision applied for labeling and feature extraction improves the robustness of the approach. Our results show the viability of using a machine learning algorithm to accurately and objectively assess acute pain among hospitalized patients.
Objective: The objective of our study was to develop and evaluate an automatic and adaptable pain assessment algorithm based on ECG features for assessing acute pain in postoperative patients likely experiencing mild to moderate pain.
Methods: The study used a prospective observational design. The sample consisted of 25 patient participants aged 18 to 65 years. In part 1 of the study, a transcutaneous electrical nerve stimulation unit was employed to obtain baseline discomfort thresholds for the patients. In part 2, a multichannel biosignal acquisition device was used as patients were engaging in non-noxious activities. At all times, pain intensity was measured using patient self-reports based on the Numerical Rating Scale. A weak supervision framework was inherited for rapid training data creation. The collected labels were then transformed from 11 intensity levels to 5 intensity levels. Prediction models were developed using 5 different machine learning methods. Mean prediction accuracy was calculated using leave-one-out cross-validation. We compared the performance of these models with the results from a previously published research study.
Results: Five different machine learning algorithms were applied to perform a binary classification of baseline (BL) versus 4 distinct pain levels (PL1 through PL4). The highest validation accuracy using 3 time domain HRV features from a BioVid research paper for baseline versus any other pain level was achieved by support vector machine (SVM) with 62.72% (BL vs PL4) to 84.14% (BL vs PL2). Similar results were achieved for the top 8 features based on the Gini index using the SVM method, with an accuracy ranging from 63.86% (BL vs PL4) to 84.79% (BL vs PL2).
Conclusions: We propose a novel pain assessment method for postoperative patients using ECG signal. Weak supervision applied for labeling and feature extraction improves the robustness of the approach. Our results show the viability of using a machine learning algorithm to accurately and objectively assess acute pain among hospitalized patients.
9.
Sarhaddi, Fatemeh; Azimi, Iman; Labbaf, Sina; Niela-Vilén, Hannakaisa; Dutt, Nikil; Axelin, Anna; Liljeberg, Pasi; Rahmani, Amir M
Long-Term IoT-Based Maternal Monitoring: System Design and Evaluation Journal Article
In: Sensors, 21 (7), pp. 2281, 2021.
BibTeX | Tags:
@article{sarhaddi2021long,
title = {Long-Term IoT-Based Maternal Monitoring: System Design and Evaluation},
author = {Fatemeh Sarhaddi and Iman Azimi and Sina Labbaf and Hannakaisa Niela-Vilén and Nikil Dutt and Anna Axelin and Pasi Liljeberg and Amir M Rahmani},
year = {2021},
date = {2021-01-01},
journal = {Sensors},
volume = {21},
number = {7},
pages = {2281},
publisher = {Multidisciplinary Digital Publishing Institute},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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